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Master of Professional Studies in
Artificial Intelligence

Faculty

  • Youakim Badr
    • Degree
      H.D.R., University of Lyon
    • Degree
      Ph.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
    • Degree
      M.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agenc
    • Degree
      M.S., Computer Science, Lebanese University
    • Degree
      B.S., Computer Science, Lebanese University

    Dr. Youakim Badr, professor of data analytics, teaches courses in data mining, deep learning, predictive analytics, and analytics programming. Dr. Badr is interested in developing a new chain of data analytical models, tools, and platforms for designing and deploying trustworthy AI service systems by leveraging his research activities built around service computing and smart services for the IoT. He is a member of Linux Foundation for AI and Data (LFAI&Data).

  • Adrian S. Barb
    • Degree
      Ph.D., Computer Science, University of Missouri
    • Degree
      MBA, Finance and Management Information Systems, University of Missouri
    • Degree
      B.S., Industrial Engineering, University of Bucharest

    Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.

  • Phillip A. Laplante
    • Degree
      Ph.D., Computer Science, Stevens Institute of Technology
    • Degree
      M.B.A., University of Colorado
    • Degree
      M.Eng., Electrical Engineering, Stevens Institute of Technology
    • Degree
      B.S., Systems Planning and Management, Stevens Institute of Technology

    Dr. Phillip A. Laplante, professor of software and systems engineering, pioneered the area of real-time image processing, co-founding the first journal and publishing the first two texts on the subject.  For these achievements, he was named a Fellow of SPIE. In AI, he has investigated uncertain information processing using fuzzy sets and rough set theory and, more recently, has focused on the use of AI in safety-critical systems. He holds an appointment as a computer scientist in the Secure Systems and Applications group at the National Institute of Standards and Technology (NIST), working on the IoT, blockchain, and related technologies.

  • Partha Mukherjee
    • Degree
      Ph.D., Information and Technology, Penn State
    • Degree
      M.S., Computer Science, University of Tulsa
    • Degree
      M.Tech., Computer Science, Indian Statistical Institute
    • Degree
      B.Eng., Mechanical Engineering, Jadavpur University

    Dr. Partha Mukherjee, assistant professor of data analytics, teaches courses in analytics programming, data mining, predictive analytics, and analytics systems design. He is a member of ACM, ACEEE, AIS, AiR, and ASE, and has published papers in peer-reviewed IEEE, Elsevier, and ACM Journals and conferences. Dr. Mukherjee’s research interests include social computing, web analytics, data mining, e-commerce, and natural language processing with a focus on text simplification.

  • Ashkan Negahban
    • Degree
      Ph.D., Industrial and Systems Engineering, Auburn University
    • Degree
      M.E., Industrial and Systems Engineering, Auburn University
    • Degree
      B.S., Industrial and Systems Engineering, University of Tehran

    Dr. Ashkan Negahban, associate professor of engineering management, performs research on stochastic simulation, statistical data analysis, and optimization techniques that advances the science of decision-making in a wide range of applications, including manufacturing, sharing economy, and supply chains. He also conducts research on the use of machine learning (ML) in simulation models as well as training and testing ML/AI algorithms via simulations. His research has been supported by the NSF, Google, Microsoft, and multiple research institutes at Penn State.

  • Colin J. Neill
    • Degree
      Ph.D., Software and Systems Engineering, University of Wales Swansea
    • Degree
      M.Sc., Communications Systems, University of Wales Swansea
    • Degree
      B.Eng., Electrical Engineering, University of Wales Swansea

    Dr. Colin Neill is a professor of software engineering and systems engineering and the head of the MPS in Artificial Intelligence program. He has an extensive background in the design, architecture, and analysis of complex systems. His AI–related work includes industrial applications of machine vision and expert systems; applications of fuzzy sets and rough set theory to uncertainty in software engineering; individual and team cognition processes; network analytics; and text mining and natural language processing of social media.

  • Robin G. Qiu
    • Degree
      Ph.D., Industrial Engineering, Penn State
    • Degree
      Ph.D., (Minor), Computer Science, Penn State
    • Degree
      M.S., Numerical Control, Beijing Institute of Technology, China
    • Degree
      B.S., Mechanical Engineering, Beijing Institute of Technology, China

    Dr. Robin G. Qiu is a professor of information science. He teaches courses on data analytics, information science, software engineering, and cyber security. His research includes data and computational sciences, health-care analytics, smart service systems (health care, city mobility, energy efficiency, IoT, etc.), blockchain, and cybersecurity analytics. He served as the editor-in-chief of INFORMS Service Science and as an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics and has more than 170 publications.

  • Dusan Ramljak
    • Degree
      Ph.D., Computer and Information Sciences, CST, Temple University
    • Degree
      M.Sc. and B.Sc., Electrical Engineering - Systems Control, University of Belgrade, Serbia

    Dr. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.

  • Raghvinder S. Sangwan
    • Degree
      Ph.D., Computer and Information Sciences, Temple University
    • Degree
      M.S., Computer Science, West Chester University
    • Degree
      B.S., Genetics and Plant Breeding, Haryana Agricultural University

    Dr. Raghvinder S. Sangwan is a professor of software engineering and director of the Big Data Lab — a research collaborative focused on data science and artificial intelligence and their applications. His own research in this space focuses on network analytics approaches to large-scale complex systems, explainable AI, and the design of secure AI systems.

  • Satish M. Srinivasan
    • Degree
      Ph.D., Information Technology, University of Nebraska at Omaha
    • Degree
      M.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
    • Degree
      B.S., Information Technology, Bharathidasan University

    Dr. Satish Srinivasan is an associate professor of information science. He teaches courses in data retrieval, processing, storage, and mining; predictive and prescriptive analytics; and the application of analytics to particular domains, including cybersecurity and sport. His research interests include natural language processing and text mining of social media; network analytics techniques to determine critical elements in large-scale networks; and the application of machine learning to bioinformatics and genomics.

  • Xi Zhang
    • Degree
      Ph.D., Physics, Stevens Institute of Technology
    • Degree
      M.S., Electrical Engineering, Shandong University
    • Degree
      B.S., Electrical Engineering, Shandong University

    Dr. Xi Zhang is an assistant professor of data analytics and teaches courses in data mining, predictive analytics, and Python programming. Her research focuses on statistical and machine learning approaches to financial modeling and prediction as well as portfolio management and optimization.

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